Evidence map›Paper›PMID 39898809›Full record

ArticleBioinformatics (Oxford, England)2025

ipd: an R package for conducting inference on predicted data.

Stephen Salerno, Jiacheng Miao, Awan Afiaz, Kentaro Hoffman, Anna Neufeld, Qiongshi Lu, Tyler H McCormick, Jeffrey T Leek

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Bioinformatics (Oxford, England), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Stephen SalernoPublic Health Sciences, Biostatistics, Fred Hutchinson Cancer Center, Seattle, WA 98109, United States.ORCID 0000-0003-2763-0494
Jiacheng MiaoBiostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI 53726, United States.ORCID 0000-0002-4524-7408
Awan AfiazPublic Health Sciences, Biostatistics, Fred Hutchinson Cancer Center, Seattle, WA 98109, United States.ORCID 0000-0001-5801-9087
Kentaro HoffmanStatistics, University of Washington, Seattle, WA 98195, United States.ORCID 0000-0003-4986-2288
Anna NeufeldMathematics and Statistics, Williams College, Williamstown, MA 01267, United States.ORCID 0000-0001-7638-0861
Qiongshi LuBiostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI 53726, United States.ORCID 0000-0002-4514-0969
Tyler H McCormickStatistics, University of Washington, Seattle, WA 98195, United States.ORCID 0000-0002-6490-1129
Jeffrey T LeekPublic Health Sciences, Biostatistics, Fred Hutchinson Cancer Center, Seattle, WA 98109, United States.ORCID 0000-0002-2873-2671

Funding

Scientific/Technical CoreP2CHD042828 · NICHD · UNIVERSITY OF WASHINGTON · PI SARA R. CURRAN · 2017 to 2026
$6.3M
Linking Variants to Multi-scale Phenotypes via a Synthesis of Subnetwork Inference and Deep LearningU01HG012039 · NHGRI · UNIVERSITY OF WISCONSIN-MADISON · PI Mark W. Craven · 2021 to 2026
$3.5M
Big Data, Big Models, and Big Bias?: A decision making framework for vital rate estimates based on extrapolationDP2MH122405 · NIMH · UNIVERSITY OF WASHINGTON · PI MCCORMICK, TYLER · 2019 to 2019
$2.3M
Data analysis tools for leveraging massive public data to improve hypothesis-driven researchR35GM144128 · NIGMS · JOHNS HOPKINS UNIVERSITY · PI Jeffrey T. Leek · 2022 to 2026
$2.2M
Improving Age- and Cause-Specific Under-Five Mortality Rates (ACSU5MR) by Systematically Accounting Measurement Errors to Inform Child Survival Decision Making in Low Income CountriesR01HD107015 · NICHD · JOHNS HOPKINS UNIVERSITY · PI Li Liu, Tyler McCormick · 2023 to 2026
$2.1M
NHGRI NIH HHS U01 HG012039NICHD NIH HHS P2C HD042828NICHD NIH HHS R01 HD107015NIGMS NIH HHS R35 GM144128NIH HHSNIMH NIH HHS DP2 MH122405
6 · The paper itself

Abstract

summaryipd is an open-source R software package for the downstream modeling of an outcome and its associated features where a potentially sizable portion of the outcome data has been imputed by an artificial intelligence or machine learning prediction algorithm. The package implements several recent proposed methods for inference on predicted data with a single, user-friendly wrapper function, ipd. The package also provides custom print, summary, tidy, glance, and augment methods to facilitate easy model inspection. This document introduces the ipd software package and provides a demonstration of its basic usage. AVAILABILITY: ipd is freely available on CRAN or as a developer version at our GitHub page: github.com/ipd-tools/ipd. Full documentation, including detailed instructions and a usage 'vignette' are available at github.com/ipd-tools/ipd.

Indexed as

Computational BiologySoftwareAlgorithmsArtificial IntelligenceMachine Learning

Identifiers

PMID39898809
PMCPMC11842045

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.